Package 'ScottKnott'

The ScottKnott Clustering Algorithm

Description

The Scott & Knott clustering algorithm is a widely used multiple comparison method in the Analysis of Variance context (Gates and Bilbro, 1978; Bony et al., 2001; Dilson et al., 2002; Jyotsna et al., 2003).

Proposed by Scott and Knott (1974), the method overcomes the overlapping problem common to other procedures such as the t-test, Tukey, Duncan, and Newman-Keuls tests. Overlapping occurs when one or more treatments are simultaneously assigned to more than one group; as the number of treatments grows to twenty or more, this ambiguity can make it virtually impossible for the experimenter to distinguish the true group structure. The Scott & Knott method does not have this problem, which is widely regarded as one of its main advantages.

The method uses a cluster analysis algorithm that, starting from the complete set of observed treatment means, recursively partitions them so that any two resulting groups are disjoint.

In their own words: “we study the consequences of using a well-known method of cluster analysis to partition the sample treatment means in a balanced design and show how a corresponding likelihood ratio test gives a method of judging the significance of difference among groups obtained”.

Monte Carlo studies suggest that the Scott & Knott method has high power and type I error rates that closely follow the nominal levels. The ScottKnott package applies this algorithm to objects of class formula, aov, aovlist, lm, and lmerMod from a prior analysis of variance, and presents results both numerically and graphically.

As of version 1.2-8, the package handles unbalanced designs using adjusted means, as described in Conrado et al. (2017).

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

References

Bony S., Pichon N., Ravel C., Durix A., Balfourier F., Guillaumin J.J. 2001. The Relationship between Mycotoxin Synthesis and Isolate Morphology in Fungal Endophytes of Lolium perenne. New Phytologist, 1521, 125-137.

Borges L.C., FERREIRA D.F. 2003. Poder e taxas de erro tipo I dos testes Scott-Knott, Tukey e Student-Newman-Keuls sob distribuicoes normal e nao normais dos residuos. Power and type I errors rate of Scott-Knott, Tukey and Student-Newman-Keuls tests under normal and non-normal distributions of the residues. Rev. Mat. Estat., Sao Paulo, 211: 67-83.

Calinski T., Corsten L.C.A. 1985. Clustering Means in ANOVA by Simultaneous Testing. Bio-metrics, 411, 39-48.

Da Silva E.C, Ferreira D.F, Bearzoti E. 1999. Evaluation of power and type I error rates of Scott-Knotts test by the method of Monte Carlo. Cienc. agrotec., Lavras, 23, 687-696.

Dilson A.B, David S.D., Kazimierz J., William W.K. 2002. Half-sib progeny evaluation and selection of potatoes resistant to the US8 genotype of Phytophthora infestans from crosses between resistant and susceptible parents. Euphytica, 125, 129-138.

Gates C.E., Bilbro J.D. 1978. Illustration of a Cluster Analysis Method for Mean Separation. Agron J, 70, 462-465.

Wilkinson, G.N, Rogers, C.E. 1973. Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 22, No. 3, pp. 392-399.

Jyotsna S., Zettler L.W., van Sambeek J.W., Ellersieck M.R., Starbuck C.J. 2003. Symbiotic Seed Germination and Mycorrhizae of Federally Threatened Platanthera PraeclaraOrchidaceae. American Midland Naturalist, 1491, 104-120.

Ramalho M.A.P., Ferreira DF, Oliveira AC 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping means in the analysis of variance. Biometrics, 30, 507-512.

Conrado, T. V., Ferreira, D. F., Scapim, C. A., and Maluf, W. R. "Adjusting the Scott-Knott cluster analyses for unbalanced designs." Crop Breeding and Applied Biotechnology 17.1 (2017): 1-9.

---

Boxplot SK Objects

Description

S3 method to plot SK objects.

Usage

## S3 method for class 'SK'
boxplot(x,
         mean.type   = c('line', 'point', 'none'),
         xlab        = NULL,
         mean.col    = 'gray',
         mean.pch    = 1,
         mean.lwd    = 1,
         mean.lty    = 1,
         args.legend = NULL, ...)

Arguments

x	A SK object.
mean.type	The type of mean representation to be plotted. The default is “line”.
xlab	A label for the ‘⁠x⁠’ axis.
mean.col	A vector of colors for the means representation.
mean.pch	A vector of plotting symbols or characters. Used only when mean.type = 'point'.
mean.lwd	Line width of mean.
mean.lty	Line type of the mean. Used only when mean.type = 'line'.
args.legend	List of additional arguments to be passed to legend; The default is NULL.
...	Optional plotting parameters.

Details

The boxplot.SK function is an S3 method for plotting SK objects. It extends the generic boxplot function by overlaying Scott & Knott group labels above the plot frame and drawing the treatment mean within each box.

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

References

Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.

See Also

boxplot

Examples

##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='SK')
##

library(ScottKnott)
data(CRD1)

## From: formula
## Simple
sk1 <- SK(y ~ x,
          data=CRD1$dfm,
          which='x')
boxplot(sk1)

## A little more elaborate
boxplot(sk1,
        mean.lwd=1.3,
        mean.col='red')

## More customisation
boxplot(sk1,
        mean.lwd=1.3,
        mean.lty=2,
        mean.col='red',
        args.legend=list(x='bottomleft'))

## With point type
boxplot(sk1,
        mean.type='point')

boxplot(sk1,
        mean.type='point',
        mean.pch=19,
        cex=1.5,
        mean.col='red')

## With a different point symbol
boxplot(sk1,
        mean.type='point',
        mean.pch='+',
        cex=2,
        mean.col='blue',
        args.legend=list(x='bottomleft'))

---

Completely Randomized Design (CRD)

Description

A list illustrating the resources of ScottKnott package related to Completely Randomized Design (‘⁠CRD⁠’).

Usage

data(CRD1)
  CRD1

Details

Simulated data for a Completely Randomized Design (‘⁠CRD⁠’) with 4 treatment levels and 6 replicates per treatment.

---

Completely Randomized Design (CRD)

Description

A list illustrating the resources of ScottKnott package related to Completely Randomized Design (‘⁠CRD⁠’).

Usage

data(CRD2)
  CRD2

Details

Simulated data for a Completely Randomized Design (‘⁠CRD⁠’) with 45 treatment levels and 4 replicates per treatment.

---

Factorial Experiment (FE)

Description

A list illustrating the resources of ScottKnott package related to Factorial Experiment (‘⁠FE⁠’).

Usage

data(FE)
  FE

Details

Simulated data for a Factorial Experiment (‘⁠FE⁠’) with 3 factors, 2 levels per factor, and 4 blocks.

---

Latin Squares Design (LSD)

Description

A list illustrating the resources of ScottKnott package related to Latin Squares Design (‘⁠LSD⁠’).

Usage

data(LSD)
  LSD

Details

Simulated data for a Latin Squares Design (‘⁠LSD⁠’) with 5 treatment levels, 5 rows, and 5 columns.

---

Plot SK objects

Description

S3 method to plot SK objects.

Usage

## S3 method for class 'SK'
plot(x,
     result         = TRUE,
     replicates     = TRUE,
     pch            = 19,
     col            = NULL,
     xlab           = NULL,
     ylab           = NULL,
     xlim           = NULL,
     ylim           = NULL,
     axisx          = TRUE,
     axisy          = TRUE,
     id.lab         = NULL,
     id.las         = 1,
     yl             = TRUE,
     yl.lty         = 3,
     yl.col         = 'gray',
     dispersion     = c('mm','sd','ci','cip'),
     d.lty          = 1,
     d.col          = 'black',
     title          = '', ...)

Arguments

x	A SK object.
result	The result of the test (letters) should be visible.
replicates	The number of replicates should be visible.
pch	A vector of plotting symbols or characters.
col	A vector of colors for the means representation.
xlab	A label for the ‘⁠x⁠’ axis.
ylab	A label for the ‘⁠y⁠’ axis.
xlim	The ‘⁠x⁠’ limits of the plot.
ylim	The ‘⁠y⁠’ limits of the plot.
axisx	If TRUE, the x axis is drawn using defaults; set to FALSE to suppress it.
axisy	If TRUE, the y axis is drawn using defaults; set to FALSE to suppress it.
id.lab	Factor level names at ‘⁠x⁠’ axis.
id.las	Factor level names written either horizontally or vertically.
yl	Horizontal (reference) line connecting the circle to the ‘⁠y⁠’ axis.
yl.lty	Line type of ‘⁠yl⁠’.
yl.col	Line color of ‘⁠yl⁠’.
dispersion	Type of dispersion bar drawn through each mean point. Options: ‘⁠mm⁠’ (min-max range), ‘⁠sd⁠’ (standard deviation), ‘⁠ci⁠’ (individual confidence interval), ‘⁠cip⁠’ (pooled confidence interval). Default is ‘⁠mm⁠’.
d.lty	Line type of dispersion.
d.col	A vector of colors for the line type of dispersion.
title	A title for the plot.
...	Optional plotting parameters.

Details

The plot.SK function is an S3 method for plotting SK objects. It generates a series of points representing the treatment means, optionally with vertical dispersion bars. The ‘⁠ci⁠’ option is calculated using each treatment's own variance as an estimate of the population variance. The ‘⁠cip⁠’ option is calculated using the mean square error (MSE) as an estimate of the population variance.

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

References

Murrell, P. (2005) R Graphics. Chapman and Hall/CRC Press.

See Also

plot

Examples

##
## Examples: Completely Randomized Design (CRD)
## More details: demo(package='ScottKnott')
##

library(ScottKnott)
data(CRD2)

## From: formula
sk1 <- with(CRD2,
            SK(y ~ x,
               data=dfm,
               which='x'))

old.par <- par(mar=c(6, 3, 6, 2))
plot(sk1,
     id.las=2)

plot(sk1,
     yl=FALSE,
     dispersion='sd',
     id.las=2)

## From: aov
av <- with(CRD2,
           aov(y ~ x,
               data=dfm))
summary(av)

sk2 <- SK(x=av,
          which='x')

col=c(rep(2, 6),
      rep(3, 36),
      rep(4, 1),
      rep(5, 2))

plot(sk2,
     dispersion='sd',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

## From: lm
av_lm <- with(CRD2,
           lm(y ~ x,
              data=dfm))

sk3 <- SK(x=av_lm,
          which='x')

par(mfrow=c(2, 1))
plot(sk2,
     dispersion='ci',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

plot(sk2,
     dispersion='cip',
     yl=FALSE,
     id.las=2,
     col=col,
     d.col=col)

par(mfrow=c(1, 1))
par(old.par)

---

Print Method for SK objects.

Description

Returns (and prints) a list for objects of class SK.

Usage

## S3 method for class 'SK'
print(x, digits = 2L,...)

Arguments

x	A given object of the class SK.
digits	Minimal number of _significant_ digits. The default is 2.
...	Further arguments (required by generic).

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

See Also

SK

Examples

data(RCBD)

sk <- with(RCBD,
       	   SK(y ~ blk + tra,
          		  data=dfm,
          		  which='tra'))
sk

---

Randomized Complete Block Design (RCBD)

Description

A list illustrating the resources of ScottKnott package related to Randomized Complete Block Design (‘⁠RCBD⁠’).

Usage

data(RCBD)
  RCBD

Details

Simulated data for a Randomized Complete Block Design (‘⁠RCBD⁠’) with 5 treatment levels and 4 blocks (one replicate per block).

---

The SK Test for Single Experiments

Description

These are methods for objects of class formula, lm, aov, aovlist and lmerMod for single, factorial, split-plot and split-split-plot experiments.

Usage

SK(x,...)

## S3 method for class 'formula'
SK(formula,
       data            = NULL,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       ...)

## S3 method for class 'lm'
SK(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       ...)

## S3 method for class 'aovlist'
SK(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       ...)

## S3 method for class 'lmerMod'
SK(x,
       which           = NULL,
       fl1             = NULL,
       fl2             = NULL,
       error           = NULL,
       sig.level       = .05,
       round           = 2,
       ...)

Arguments

x, formula	An object of class formula, lm, aov, aovlist, or lmerMod. Objects of the formula class follow “response variable ~ predictor variables”.
data	An object of class data.frame. Used only when the input is of class formula.
which	The name of the treatment factor to be compared. Must be quoted.
fl1	An integer of length 1 selecting the level of the first nesting factor.
fl2	An integer of length 1 selecting the level of the second nesting factor.
error	The error term to be used. For split-plot and split-split-plot experiments, see Details.
sig.level	Significance level used in the SK algorithm to form the groups of means. Default is 0.05.
round	Integer indicating the number of decimal places. Default is 2.
...	Potential further arguments (required by generic).

Details

The function SK returns an object of class SK containing the groups of means and the variables needed by summary and plot.

The error argument may be used whenever a specific error term other than the residual is required. In split-plot and split-split-plot experiments, error terms may be combined using / in the order implied by the which argument. For example, for an aovlist object from a block split-plot experiment, a valid combination would be error = 'Within/blk:plot' with which = 'subplot:plot'.

Value

The function SK returns a list of the class SK with the slots:

out	A list storing the result of Scott & Knott test.
info	A list storing the descriptive statistics.
stat	A matrix with the statistics of each clustering process.
clus	A list with the groups formed in each clustering process.

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

References

Miller, R.G. (1981) Simultaneous Statistical Inference. Springer.

Ramalho M.A.P, Ferreira D.F and Oliveira A.C. (2000) Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.

Steel, R.G., Torrie, J.H and Dickey D.A. (1997) Principles and procedures of statistics: a biometrical approach. Third Edition.

Yandell, B.S. (1997) Practical Data Analysis for Designed Experiments. Chapman and Hall.

Examples

##
## Examples: Randomized Complete Block Design (RCBD)
## More details: demo(package='ScottKnott')
##

## Input classes accepted: formula, aov, lm, aovlist, and lmerMod.

data(RCBD)

## From: formula
sk1 <- with(RCBD,
            SK(y ~ blk + tra,
               data=dfm,
               which='tra'))
summary(sk1)

## From: lmerMod
## This class requires the lme4 package.
## Not run: 
  if(require(lme4)){
    lmer1 <- with(RCBD,
                  lmer(y ~ (1|blk) + tra,
                       data=dfm))

    sk2 <-  SK(lmer1,
               which='tra')
    summary(sk2)
  }

## End(Not run)
##
## Example: Latin Squares Design (LSD)
## More details: demo(package='ScottKnott')
##

data(LSD)

## From: formula
sk3 <- with(LSD,
            SK(y ~ rows + cols + tra,
               data=dfm,
               which='tra'))
summary(sk3)

## From: aov
av1 <- with(LSD,
            aov(y ~ rows + cols + tra,
                data=dfm))

sk4 <- SK(av1,
              which='tra')
summary(sk4)

## From: lm
lm1 <- with(LSD,
            lm(y ~ rows + cols + tra,
               data=dfm))

sk5 <- SK(lm1,
          which='tra')
summary(sk5)

##
## Example: Factorial Experiment (FE)
## More details: demo(package='ScottKnott')
##

data(FE)
## From: formula
## Main factor: N
sk6 <- with(FE,
            SK(y ~ blk + N*P*K,
               data=dfm,
               which='N'))
summary(sk6)

## Nested: p1/N
## From: formula
n_sk1 <- with(FE,
              SK(y ~ blk + N*P*K,
                 data=dfm,
                 which='P:N',
                 fl1=1))
summary(n_sk1)

## Nested: p2/N
## From: lm
lm2 <- with(FE,
            lm(y ~ blk + N*P*K,
               dfm))

n_sk2 <- with(FE,
              SK(lm2,
                 which='P:N',
                 fl1=2))
summary(n_sk2)

## Nested: n1/P
## From: aov
av2 <- with(FE,
            aov(y ~ blk + N*P*K,
                dfm))

n_sk3 <- with(FE,
              SK(av2,
                 which='N:P',
                 fl1=1))
summary(n_sk3)

## From: lmerMod
## Not run: 
  if(require(lme4)){
    lmer2 <- with(FE,
                  lmer(y ~ (1|blk) + N*P*K,
                       dfm))

    n_sk4 <- with(FE,
                  SK(lmer2,
                     which='N:P',
                     fl1=1))
    summary(n_sk4)
  }

## End(Not run)

##
## Example: Split-Plot Experiment in Time (SPET)
## More details: demo(package='ScottKnott')
##
data(SPET)

## From: lm
lm3 <- with(SPET,
            lm(y ~ blk*tra + tra*year,
               dfm))

## Nested: crotgrantiana/year
sp_sk1 <- SK(lm3,
             which='tra:year',
             fl1=1)
summary(sp_sk1)

## Nested: year1/tra
## It is necessary to set the year error with the treatment error,
## in the order of the which argument.
## It is necessary to specify how to combine the error terms.
sp_sk2 <-  SK(lm3,
              which='year:tra',
              error='Residuals/blk:tra',
              fl1=1)
summary(sp_sk2)

## From: lmerMod
## Main factor: tra
## Not run: 
  if(require(lme4)){
    lmer3 <- with(SPET,
                  lmer(y ~ blk + (1|blk:tra) + tra*year,
                       dfm))

    ## Main factor: tra
    sp_sk3 <- SK(lmer3,
                 which = 'tra',
                 error = 'blk:tra')
    summary(sp_sk3)

    ## Nested: year1/tra
    sp_sk4 <- SK(lmer3,
                 which='year:tra',
                 error='Residual/blk:tra',
                 fl1=1)
    summary(sp_sk4)
  }

## End(Not run)

## Example: Split-Split-Plot Experiment (SSPE)
## More details: demo(package='ScottKnott')
##

data(SSPE)
## From: formula
## Main factor: P
## It is necessary to specify the appropriate error term for the test.
ssp_sk1 <- with(SSPE,
                SK(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                   data=dfm,
                   which='P',
                   error='blk:P'))
summary(ssp_sk1)

## Main factor: SP
## It is necessary to specify the appropriate error term for the test.
ssp_sk2 <- with(SSPE,
                SK(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                   data=dfm,
                   which='SP',
                   error='blk:P:SP'))
summary(ssp_sk2)

## Main factor: SSP
ssp_sk3 <- with(SSPE,
                SK(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                   data=dfm,
                   which='SSP'))
summary(ssp_sk3)

## From: aov
## Main factor: SSP
av3 <- with(SSPE,
            aov(y ~ blk + P*SP*SSP + Error(blk/P/SP),
                data=dfm))

ssp_sk4 <- SK(av3,
              which='SSP')
summary(ssp_sk4)

## Nested: p1/SP
## It is necessary to specify the appropriate error term for the test.
ssp_sk5 <- SK(av3,
              which='P:SP',
              error='blk:P:SP',
              fl1=1)
summary(ssp_sk5)

## Nested: p1/SSP
ssp_sk6 <- SK(av3,
              which='P:SSP',
              fl1=1)
summary(ssp_sk6)

## Nested: p1/sp1/SSP
## Testing SSP within level one of P and level one of SP.
ssp_sk7 <- SK(av3,
              which='P:SP:SSP',
              fl1=1,
              fl2=1)
summary(ssp_sk7)

## Nested: p2/sp1/SSP
ssp_sk8 <- SK(av3,
              which='P:SP:SSP',
              fl1=2,
              fl2=1)
summary(ssp_sk8)

## Nested: sp1/P
## It is necessary to specify the appropriate error term for the test.
ssp_sk9 <- SK(av3,
              which='SP:P',
              error='blk:P:SP/blk:P',
              fl1=1)

summary(ssp_sk9)

## Nested: ssp1/SP
ssp_sk10 <- SK(av3,
               which='SSP:SP',
               error='Within/blk:P:SP',
               fl1=1)
summary(ssp_sk10)

## Nested: ssp1/sp1/P
## It is necessary to specify the appropriate error term for the test.
ssp_sk11 <- SK(av3,
               which='SSP:SP:P',
               error='Within/blk:P:SP/blk:P',
               fl1=1,
               fl2=1)
summary(ssp_sk11)

## UNBALANCED DATA
## Means are adjusted using the Least-Squares Means (LSMeans) methodology.
## From: formula
data(CRD2)

uCRD2 <- CRD2$dfm
uCRD2[c(3, 5, 10, 44, 45), 3] <- NA

usk1 <-  SK(y ~ x,
            data=uCRD2,
            which='x')
summary(usk1)

## From: lm
ulm1 <- lm(y ~ x,
           data=uCRD2)

usk2 <- SK(ulm1,
           which='x')
summary(usk2)

## Factorial Experiments
## Nested: p1/N
## From: lm

uFE <- FE$dfm
uFE[c(3, 6, 7, 20, 31, 32), 5] <- NA

ulm2 <- lm(y ~ blk + N*P*K,
           uFE)

## Nested: p1/N
usk3 <- SK(ulm2,
           data=uFE,
           which='P:N',
           fl1=1)
summary(usk3)

## Nested: p2/n2/K
usk4 <- SK(ulm2,
           data=uFE,
           which='P:N:K',
           fl1=2,
           fl2=2)
summary(usk4)

---

Sorghum Yield: Balanced Squared Lattice Design

Description

The experiment consists of 16 treatments (cultivars) of sorghum conducted in a balanced squared lattice design and the yield by plot (kg/plot).

Usage

data(sorghum)
  sorghum

Format

An incomplete balanced block design with 4 blocks, 16 treatments, and 5 repetitions, that is, the yield of each treatment is measured 5 times. sorghum is a list with 4 elements. The first ‘⁠tr⁠’ is a factor of length 80 with 16 levels describing the treatments. The second ‘⁠dm⁠’ is data.frame describing the design matrix. Its columns are ‘⁠x⁠’, ‘⁠bl⁠’ (blocks) and ‘⁠r⁠’ repetitions. The third ‘⁠y⁠’ is a numeric vector the yields. The fourth ‘⁠dfm⁠’ is a data frame with four columns. The first three columns are the design matrix and the fourth is ‘⁠y⁠’.

Details

The experiment was conducted at EMBRAPA Milho e Sorgo (The Brazilian Agricultural Research Corporation, Corn and Sorghum section).

Source

Ramalho, M.A.P. and Ferreira, D.F. and Oliveira, A.C. (2000). Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, Lavras, Brazil, page 167.

Examples

library(ScottKnott)

data(sorghum)

av <- aov(y ~ r/bl + x,
          data=sorghum$dfm)

sk <- SK(av,
         which='x',
         sig.level=0.05)

summary(sk)

plot(sk)

---

Split-Plot Experiment (SPE)

Description

A list illustrating the resources of ScottKnott package related to Split-Plot Experiment (‘⁠SPE⁠’).

Usage

data(SPE)
  SPE

Details

Simulated data for a Split-Plot Experiment (‘⁠SPE⁠’) with 3 whole plots, 4 sub-plot treatments, and 6 replicates per sub-plot.

---

Split-plot Experiment in Time (SPET)

Description

The experiment consists of 8 treatments (7 leguminous cover crops and maize) in a Randomized Complete Block Design (‘⁠RCBD⁠’) and the yield by plot (kg/plot).

Usage

data(SPET)
  SPET

Source

Gomes, F.P. (1990). Curso de Estatistica Experimental. 13 ed. Editora NOBEL, Piracicaba, Brazil, page 157.

---

Split-Split-Plot Experiment (SSPE)

Description

A list illustrating the resources of ScottKnott package related to Split-Split-Plot Experiment (‘⁠SSPE⁠’).

Usage

data(SSPE)
  SSPE

Details

Simulated data for a Split-Split-Plot Experiment (‘⁠SSPE⁠’) with 3 whole plots, 3 sub-plot treatments, 5 sub-sub-plot treatments, and 4 replicates per sub-sub-plot.

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Summary Method for SK Objects

Description

Returns (and prints) a summary list for SK objects.

Usage

## S3 method for class 'SK'
summary(object,
 ...)

Arguments

object	A given object of the class SK.
...	Potential further arguments (required by generic).

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

References

Chambers, J.M. and Hastie, T.J. (1992) Statistical Models in S. Wadsworth and Brooks/Cole.

See Also

SK

Examples

##
  ## Examples: Completely Randomized Design (CRD)
  ## More details: demo(package='ScottKnott')
  ##

  data(CRD2)
  ## From: formula
  sk1 <- with(CRD2,
              SK(y ~ x,
                 data=dfm,
                 which='x',
                 id.trim=5))
  summary(sk1)

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Create a Table for Export

Description

This function is re-exported from the xtable package so that xtable() is available after library(ScottKnott) without requiring a separate library(xtable) call.

For SK objects the S3 method xtable.SK is dispatched automatically. For full documentation of the generic see help("xtable", package = "xtable").

See Also

xtable.SK, xtable

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xtable method for SK objects.

Description

Convert an SK object to an xtable.SK object, which can then be printed as a LaTeX or HTML table. This function provides an additional method for the xtable function from the xtable package.

Usage

## S3 method for class 'SK'
xtable(x, ...)

Arguments

x	A given object of the class SK.
...	Further arguments (required by xtable::xtable).

Author(s)

Faria, J. C. ([email protected])
Jelihovschi, E. G. ([email protected])
Allaman, I. B. ([email protected])

See Also

xtable

Examples

data(RCBD)

lm1 <- with(RCBD,
            lm(y ~ blk + tra,
               data=dfm))

sk1 <- SK(lm1,
          which='tra')

tb <- xtable(sk1)
## Not run: 
  print(tb)

## End(Not run)

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